Abstract: eastern part of watershed. On the other side

Abstract: Accelerated
rate of soil erosion is a serious and continuous endemic environmental problem
in the western part of West Bengal. The present study is carried out in upper
Kangsabati watershed with an area of 276.19 km2. It is fact that the
surface runoff of seasonal rainfall is more in this area due to its undulating
terrain characteristics. Average annual soil loss has been estimated based on
the five parameters defined in the Revised Universal Soil Loss Equation (RUSLE)
and with the help of Geographical Information technology. Overlay of five
parameters, i.e. rainfall–runoff erosivity factor (R), soil erodibility factor
(K), slope length and steepness factor (LS), cover and management factor (C)
and support and conservations practices factor (P) has been done in GIS
platform. Predicted average annual soil loss of the basin has been classified
into four categories. High rate of soil erosion (>15 t ha-1 year -1)
was found along the north eastern part of watershed. On the other side low
amount of soil erosion (<1 t ha-1 year -1) was found along the hilly tract of dense forest cover and plantation areas.  Keywords: RUSLE, Soil Erosion, GIS, Watershed, Terrain, Hilly Tract Introduction One of the most serious and continuous environmental problem is soil erosion and land degradation particularly in third world country where agriculture is the main economic activity.  More than 50% of the total area of India is affected by land degradation resulting from soil erosion (Sehgal and Abrol 1994). It is now established fact that each year more than 75 billion tons of soil is removed from agricultural land due to erosion (Pandey et al. 2009). Broadly acceptable to the geomorphologists, erosion is the progressive removal of soil or rock particles from the parent mass by a fluid agent (Strahler 1964). The forms of soil erosion are mainly sheet, rill and gully erosion. But this type of erosion widely varied in different spatial and temporal scale depending on morpho-climatic and pedo-geomorphic factors (Ghosh and Maji 2011). The amount of soil erosion is measured on the basis of two models i.e. physical based and empirical based model.  GIS and remote sensing (RS) provide spatial input data to the empirical model and predict the potential soil erosion rate. By using different type of models many scholar have worked on soil erosion throughout the world. The most commonly used empirical model is Universal Soil Loss Equation (USLE) developed by Wischmeier and Smith in 1965 for measuring sheet and rill erosion (Ganasri and Ramesh 2016). Revised universal Soil Loss Equation (RUSLE) uses the same empirical principles as USLE; however it includes numerous improvements, such as monthly factors, incorporation of the influence of profile convexity/concavity using segmentation of irregular slopes, improved empirical equations for the computation of LS factor (Foster and Wischmeier1974, Renard et al. 1991).  In this study LISS III image has been used for generating C factor by using Normalized Difference Vegetation Index (NDVI). The present study is an attempt to focus on the estimation of soil erosion in the upper Kangsabati watershed by RUSLE model. Location and description of the study area The upper Kangsabati (also known as Kasai) watershed is located in Puruliya district in the state of West Bengal, India (Fig. 1). It is extended between 23°13'26"N to 23°28'33"N latitude and 85°17'18"E to 86°11'56" E longitude. The catchment area of the study site is about 276.19 km2. It originates near Jhalda in Chotanagpur plateau of Puruliya district. Regionally the study area is a part of Chotanagpur Gnessic Complex (CGC), in which rock formation belongs to Archaean age, which is the oldest rock formation of the district (Halder and Saha, 2015).  The soils in the study area are mainly fine loamy, coarse loamy and loamy skeletal (NBSS&LUP, 2008).  The average annual rainfall in this watershed is 1393 mm and annual mean temperature is 25.6°C with mean summer and mean winter temperature are 29.0°C and 21.3°C respectively (Saini et al. 1999). Topographically the area is characterized by undulating rugged hilly terrain. Materials and methods In this study, GIS plays a major role to prepare different types of thematic map and estimation of soil erosion rate.  For the present study different type of data are collected from different sources. These are mainly numerical data, thematic maps etc, which help to analysis the research work. Those data are mainly, rainfall data of five years (2012-2016) from India Meteorological Department (IMD), soil data from National Bureau of Soil Survey and Land Use Planning (NBSS & LUP, 2010), topographical sheets (73I/3, 73I/4, and 73E/15, scale of 1:50000) from Survey of India (SOI), Shuttle Radar Topography Mission (SRTM) 30 m resolution and IRS P6 LISS III satellite image. ArcGIS 10.3 and ERDAS Imagine 14.0 were used for creation of digital database, data integration and analysis. After correction the DEM in GIS platform, it was used to prepare the slope map. Later DEM and slope map were used to prepare LS factor. IRS P6 LISS III data along with topographical sheets were used to prepare detailed landuse landcover (LULC) map. Soil erosion estimation model RUSLE The Revised Universal Soil Loss Equation (RUSLE) model was adopted to estimate the annual soil loss and the equation (Eq. 1) is as: A = R × K × L × S × C × P                                                                                                                                   (1)          Where, A is the average annual soil loss per unit area expressed in tones/ha/year (t ha-1 year -1); R is the rainfall–runoff erosivity factor (MJ mm ha-1 h-1); K is soil erodibility factor (t ha h MJ-1 mm-1); L is the slope length factor; S is the slope steepness factor; C is the cover and management factor and P is the support and conservation practices factor.  Flow chart (Fig. 2) showing the methodology adopted in this research study. Rainfall                 erosivity factor (R) Rainfall erosivity factor is the annual total value of the erosion index (EI30) for a particular location (Sarkar et al. 2005).  Rainfall intensity represents the principal factor of kinetic energy and to estimate the rainfall erosivity (Balasubramani et al. 2015). If the intensity and rainfall increases the value of R factor also increases. In this study, Singh et al. (1981) established empirical equation (Eq. 2) has been used for estimating annual rainfall erosivity. The linear relationship of erosion index is: Ra = 79 + 0.363 × P                                                                                                                                                            (2)           Where, Ra is the average annual rainfall erosivity factor (MJ mm ha-1 h-1) and P is the rainfall (mm). In this study, 5 years (2012 – 2016) average annual rainfall data from India Meteorological Department (IMD) has been used for calculating R factor (Table 1).  The average annual rainfall data collected from four rain-gauge stations namely Jhalda, Jaipur, Arsha and Baghmandi located in upper Kangsabati watershed. Spatial distribution of R factor has been obtained using Inverse Distance Weighted (IDW) interpolation techniques.                  Soil erodibility factor (K) Soil erodibility factor is a measure of potential erodibility of soil and it depends on the inherent properties of the soil. The K factor is related to the integrated effects of rainfall, runoff and infiltration on soil loss, accounting for the influences of soil properties on soil loss during storms action on uplands areas (Renard et al. 1997). Soil erodibility factor map has been derived based on different soil types, texture, organic matter and permeability. On the basis of the district level pedological map derived from National Bureau of Soil Survey and Land Use Planning (ICAR, 2008), K values of different soil type in the study area have been estimated. Thirteen types of soil classes (Table 2) in the study area have been identified and values are imputed to respective classes of soil.   Topographic factor (LS) Topographic factor includes slope length factor (L) and slope steepness factor (S) mainly reflect the effect of surface topography on erosion by water action (Yildirim 2012; Shit et al. 2015; Sarkar et al. 2005). Slope length (L) and slope steepness (S) have been derived from SRTM DEM (30 m resolution) in ArcGIS 10.3 platform. Slope length factor (L) has been calculated on the basis of following formula (Eq. 3) given by McCool et al. (1987) is:                                                                                                                                                                          (3)           Where, L is the slope length factor; ? is the slope length in meter; m is the variable slope-length exponent. 22.13 is the RUSLE unit plot length in meter. The slope steepness factor (S) is evaluated based on the relationship (Eqs. 4.1, 4.2) given by McCool et al. (1987) for slope length longer than 4 meter:                                                                                                                                     (4.1)                                                                                                                                (4.2) Where, S is the slope steepness factor which is dimensionless and  is the slope angle in degree. The LS factor is calculated by multiplying L and S factor together (Moore and Burch, 1986) in raster calculator in ArcGIS platform with the help of following equation (Eq. 5):                        (5)